I work in the canvas and have a problem with using my learned SVMs for classifying more data. I really hope that someone can explain how I do this, as I have been unable to learn from the documentation and the python sources.

Basically, I work in the Canvas, where I have two datasets, A and B, over the same domain. I train a classifier, c, on A, and then want to apply it to the instances in B. I do this with a python script node, that basically does

This works for most classifiers, but not the SVM, as that transforms the input domain to its own normalized feature space. What I cannot see, is how that I can transform the cases in B in the same manner. How do I get at the "normalization transform" and apply it to my cases?

Ah. Thank you. I thought the domain-attribute of the classifier corresponded to the mathematical term "domain" - i.e. the legal input - which managed to confused me so much that I didn't thought of applying my cases directly.